Dasgupta Indrasis, Das Totan, Das Biplab, Gayen Shovanlal
Laboratory of Drug Design and Discovery, Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India.
Beilstein J Nanotechnol. 2024 Jul 22;15:909-924. doi: 10.3762/bjnano.15.75. eCollection 2024.
Nanoparticles (NPs) are considered as versatile tools in various fields including medicine, electronics, and environmental science. Understanding the structural aspects of surface modifiers in nanoparticles that govern their cellular uptake is crucial for optimizing their efficacy and minimizing potential cytotoxicity. The cellular uptake is influenced by multiple factors, namely, size, shape, and surface charge of NPs, as well as their surface functionalization. In the current study, classification-based ML models (i.e., Bayesian classification, random forest, support vector classifier, and linear discriminant analysis) have been developed to identify the features/fingerprints that significantly contribute to the cellular uptake of ENMOs in multiple cell types, including pancreatic cancer cells (PaCa2), human endothelial cells (HUVEC), and human macrophage cells (U937). The best models have been identified for each cell type and analyzed to detect the structural fingerprints/features governing the cellular uptake of ENMOs. The study will direct scientists in the design of ENMOs of higher cellular uptake efficiency for better therapeutic response.
纳米颗粒（NPs）被认为是医学、电子学和环境科学等各个领域的通用工具。了解纳米颗粒中表面修饰剂的结构方面对其细胞摄取的影响，对于优化其功效和最小化潜在细胞毒性至关重要。细胞摄取受多种因素影响，即纳米颗粒的大小、形状、表面电荷及其表面功能化。在当前研究中，已开发出基于分类的机器学习模型（即贝叶斯分类、随机森林、支持向量分类器和线性判别分析），以识别对包括胰腺癌细胞（PaCa2）、人内皮细胞（HUVEC）和人巨噬细胞（U937）在内的多种细胞类型中工程纳米材料（ENMOs）的细胞摄取有显著贡献的特征/指纹。已为每种细胞类型确定了最佳模型，并进行分析以检测控制ENMOs细胞摄取的结构指纹/特征。该研究将指导科学家设计具有更高细胞摄取效率的ENMOs，以获得更好的治疗反应。
